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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry 5

Monitoring and Forecasting Quality Indicators of Predough Pr...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Zhyltsov, Andrii Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350384499

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them. ©2024 IEEE.

关键词： Bakeries

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Action Masked Deep Reinforcement learning for controlling Industrial Assembly Lines

Action Masked Deep Reinforcement learning for Controlling In...

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AI IoT Congress (AIIoT), World

作者： Ali Mohamed Ali Luca Tirel Dept. of Mechanical and Aerospace Engineering Carleton University Ottawa Canada Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Rome Italy

The scalability of control algorithms controlling industrial assembly lines either model-based or model-free is still a big barrier from the point of view of implementation. In this paper, various Deep Reinforcement learning (DRL) algorithms will be tested in a virtual environment of a generic industrial assembly line in terms of optimally with respect to optimal exact solutions, and computational time. The agents will take bigger rewards when they minimize the finishing time while maintaining constraints, such as time precedence between tasks. The control actions will be modeled as a task assignment matrix that assigns the tasks to be completed to specific workstations. Another control action is the resource allocation. Due to the big number of constraints, many actions will be unfeasible leading to a longer time of training of the agent to learn not to apply unfeasible actions, therefore action masking will be introduced to the DRL algorithms to reduce the action space only to the feasible actions, thereby significantly reducing training time. Proximal Policy Optimization (PPO) agent shows faster and more stable convergence to the optimal solution over the Deep-Q Network (DQN) family, including the double DQN and Dueling one.

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Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease

Devising a Set of Compact and Explainable Spoken Language Fe...

引用

International Symposium on Chinese Spoken Language Processing

作者： Junan Li Yunxiang Li Yuren Wang Xixin Wu Helen Meng Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explain-able and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model. Our experimental results show that the newly proposed features consistently outperform traditional linguistic features across two different classifiers with high dimension efficiency. Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.

关键词： Visualization Scalability Large language models Linguistics Aging Feature extraction Alzheimer's disease

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Study on Residents' Intentions to Purchase Prefabricated Housing in China

Study on Residents' Intentions to Purchase Prefabricated Hou...

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2024 International Conference on Construction and Real Estate management: ESG Development in the Construction Industry, ICCREM 2024

作者： Zhou, Fanxi Zhou, Zhenzong Man, Qingpeng Wang, Yaowu School of Economics and Trade Henan Univ. of Technology Henan China Dept. of Construction Management Harbin Institute of Technology Harbin China Dept. of Construction Management Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Currently, with an increasingly serious environmental population and energy crisis, countries are actively exploring measures to reduce carbon emissions. Environmental protection and sustainable development have become hot points for research. As a high-consumption industry, the construction industry urgently needs to take measures to reduce its negative impacts on the environment. Prefabricated housing has the characteristic of a short construction period and is environmentally friendly. Therefore, it plays a vital role in China's sustainable development. At the same time, the residential industry is a key area for the development of prefabricated buildings. In order to better reveal the consumers' purchase intentions and promote the deeper development of prefabricated housing to bring greater social benefits, this study combines the theory of planned behavior (TPB) and the norm activation model (NAM) to explain the influencing factors of consumers' purchase of prefabricated housing, hoping to scientifically guide residents, and at the same time provide theoretical reference for policy formulation. © ASCE.

关键词： Carbon sequestration

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On Detecting Low-Pass Graph Signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： Array signal processing Conferences Low-pass filters Signal processing algorithms Detectors Filtering algorithms Complexity theory

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An Innovative Approach: Typicality-Based Collaborative Filtering for Recommender Systems 3

An Innovative Approach: Typicality-Based Collaborative Filte...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Talukdar, Veera Veeraiah, Vivek Roy, Devika Rani Pandey, Jay Kumar Gupta, Ankur Dhabliya, Dharmesh D y Patil International University Dept. of Computer Science Maharashtra Pune India Sri Siddhartha Academy of Higher Education Dept. of Computer Science Karnataka Tumkur India K. C. College of Engineering & Management Studies & Research Dept. of It Thane India Shri Ramswaroop Memorial University Dept. of Ece UP Barabanki India Vaish College of Engineering Dept. of Cse Haryana Rohtak India Vishwakarma Institute of Information Technology Dept. of It Maharashtra Pune India

ISBN: (纸本)9798350330724

There are two types of collaborative filtering (CF) methods: user-based and item-based. CF is a frequently used technique for recommender systems. Item-based CF concentrates on making suggestions based on strong correlations, whereas user-based CF concentrates on discovering comparable user preferences and doing so. Today, the majority of CF approaches employ corated objects to determine how similar users are. This study suggests a typicality-based method called TyCo that identifies users who are 'neighbors' of one another by looking at user group typicality. TyCo represents a user through a user typicality vector, which identifies their preferences for various goods. In order to get around the drawbacks of conventional collaborative filtering techniques, it chooses 'neighbors' by comparing users' similarity based on their typicality levels. This is the first piece of work to use typicality to collaborative filtering, and it runs experiments to assess and show off its benefits. The cost of preprocessing steps, which include creating user prototypes and assessing user typicality in user groups, varies based on the clustering approach selected. © 2023 IEEE.

关键词： Collaborative filtering

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Towards An Automatic Construction Of Simulation Scenarios: A Systematic Review

Towards An Automatic Construction Of Simulation Scenarios: A...

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2023 Winter Simulation Conference, WSC 2023

作者： Davis, Christopher W.H. Jetter, Antonie J. Giabbanelli, Philippe J. Microsoft 1414 NW Northrup PortlandOR97209 United States Portland State University Dept. of Engineering & Technology Management PortlandOR97201 United States Miami University Dept. of Computer Science & Software Engineering OxfordOH45056 United States

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods. © 2023 IEEE.

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Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML 1

Optimization Ensemble Learning Techniques for Reliable Crop ...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Upreti, Kamal Lingareddy, Nagulapalli Deepika, Sheri Kumar, Nishant Parashar, Jyoti Divakaran, Prakash Dept. of Computer Science Delhi NCR India Vignan Institute of Technology and Science Department of Cse Telangana India Sreyas Institute of Engineering and Technology Department of Cse Telangana Hyderabad India School of Business and Management Bangalore India Adgitm Dept. of Computer Science & Engg India Himalayan University Arunachal Pradesh Itanagar India

ISBN: (纸本)9798350392111

The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Bioinformatics using DNA Cryptography 8

A Review on Bioinformatics using DNA Cryptography

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kairi, Animesh Bhadra, Tapas Karmakar, Samit Verma, Aman Saha, Sonia Gupta, Soumili Chowdhury, Souraja Aliah University Computer Science Kolkata India University of Engineering & Management Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798331520762

DNA-based encryption with bioinformatics has emerged as a promising field for secure data storage and transfer. This study analyzes the bioinformatics aspects of DNA cryptography, focusing on important advancements and challenges. We look at techniques like DNA origami, enzymatic reactions, and DNA strand displacement that use DNA sequences to encode and decode data. Additionally discussed are the security implications of DNA-based cryptography, including privacy issues, data integrity issues, and the potential for DNA-based attacks. Finally, we outline some possible directions for further review, such as developing more robust error-correction systems, looking at cutting-edge DNA-based encryption methods using bioinformatics, and addressing the ethical and societal implications of this emerging technology. © 2025 IEEE.

关键词： Bioinformatics

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Power Supply of the Detector Faults Device from the Current of a Single-Phase-to-Earth Fault on Tower of 10kV Overhead Line with Isolated Neutral 7

Power Supply of the Detector Faults Device from the Current ...

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7th IEEE International Conference on Smart Technologies in Power engineering and Electronics, STEE 2024

作者： Bezruchko, Viacheslav Buinyi, Roman Strohii, Andrii Miroshnyk, Oleksandr Demchenko, Mariia Chernihiv Politechnic National University Dept. Electrical Power Engineering and Information Measuring Technologies Chernihiv Ukraine Chernihiv Politechnic National University Dept. Electrical Power Engineering and Information Measuring Technologies Chernihiv Ukraine State Biotechnological University Dept. Electricity Supply and Energy Management Kharkiv Ukraine Ntu of Ukraine"I. Sikorsky Kyiv Polytechnic Institute" Dept. Computer-Integrated Technologies of Device Production Kyiv Ukraine

ISBN: (纸本)9798331540999

The article describes a method for calculating a special transformer that will make it possible to supply power to devices for identifying single-phase-to-ground faults in overhead power lines with an isolated neutral from the fault current itself. For such a transformer, it is proposed to use the armature of grounding circuit of the 6-10 kV overhead line tower as the primary winding, which will allow supply energy without violating the integrity of the grounding. The proposed method makes it possible to calculate the operating mode parameters of such a transformer at different values of fault currents in the network, its overall characteristics and magnetic circuit parameters. It is shown that the amount of energy obtained in this way is small for the operation of the transmitter and it is proposed to first accumulate a certain amount of energy before sending a message. The maximum time for accumulating a sufficient amount of energy is calculated at the minimum possible fault current of 2A. This time can reach 10 minutes, which is quite short, since the emergence crew needs approximately this amount of time to prepare to go to the damage site. © 2024 IEEE.

关键词： Overhead lines

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